Quartet Inference from SNP Data Under the Coalescent Model

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1 Bioinformatics Advance Access published August 7, 2014 Quartet Inference from SNP Data Under the Coalescent Model Julia Chifman 1 and Laura Kubatko 2,3 1 Department of Cancer Biology, Wake Forest School of Medicine, Winston-Salem, NC, Department of Statistics, The Ohio State University, Columbus, OH Department of Evolution, Ecology, and Organismal Biology, The Ohio State University, Columbus, OH Associate Editor: Prof. David Posada ABSTRACT Motivation: Increasing attention has been devoted to estimation of species-level phylogenetic relationships under the coalescent model. However, existing methods either use summary statistics (gene trees) to carry out estimation, ignoring an important source of variability in the estimates, or involve computationally-intensive Bayesian Markov chain Monte Carlo algorithms that don t scale well to whole-genome data sets. Results: We develop a method to infer relationships among quartets of taxa under the coalescent model using techniques from algebraic statistics. Uncertainty in the estimated relationships is quantified using the nonparametric bootstrap. The performance of our method is assessed with simulated data. We then describe how our method could be used for species tree inference in larger taxon samples, and demonstrate its utility using data sets for Sistrurus rattlesnakes and for soybeans. Availability and Implementation: The method to infer the phylogenetic relationship among quartets is implemented in the software SVDquartets, available at lkubatko/software/svdquartets. Contact: Laura Kubatko, lkubatko@stat.osu.edu 1 INTRODUCTION With recent advances in DNA sequencing technology, it is now common to have available alignments from multiple genes for inference of an overall species-level phylogeny. While this species tree is generally the object that we seek to estimate, it is widely known that each individual gene has its own phylogeny, called a gene tree, which may not agree with the species tree. Many possible causes of this gene incongruence are known, including horizontal gene transfer, gene duplication and loss, hybridization, and incomplete lineage sorting (ILS) (Maddison, 1997). Of these, the best studied is incomplete lineage sorting, which is commonly modeled by the coalescent process (Kingman, 1982a,b; Liu et al., 2009a). Much recent effort has been devoted to the development of methods to estimate species-level phylogenies from multi-locus data under the coalescent model (Liu and Pearl, 2007; Liu et al., to whom correspondence should be addressed 2009b; Kubatko et al., 2009; Heled and Drummond, 2010; Than and Nakhleh, 2009; Bryant et al., 2012). Here we consider this basic problem, although our approach to the problem differs from previous approaches in several important ways. Previous approaches can be divided into two groups (Liu et al., 2009a): summary-statistics approaches and sequence-based approaches. Summary-statistics approaches first estimate a gene tree independently for each gene, and then treat the estimated gene trees as data for a second stage of analysis to estimate the species tree. The most popular approaches in this category are Maximum Tree (Liu et al., 2009b) (also implemented in the program STEM (Kubatko et al., 2009)), STAR (Liu et al., 2009c), STEAC (Liu et al., 2009c), MP-EST (Liu et al., 2010), and Minimize Deep Coalescences (MDC; as implemented in the program PhyloNet (Than and Nakhleh, 2009)). These methods are computationally efficient for large data sets, but generally ignore variability in the estimated gene trees and thus potentially lose accuracy. The second group of methods uses the full data for estimation of the species tree via a Bayesian framework for inference. The three most common methods in this group, BEST (Liu and Pearl, 2007), *BEAST (Heled and Drummond, 2010), and SNAPP (Bryant et al., 2012), all seek to estimate the posterior distribution for the species tree using Markov chain Monte Carlo (MCMC), but differ in some details of the implementation. These methods become time-consuming when the number of loci is large, and assessment of convergence of the MCMC can be difficult. Our proposed method is distinct from both classes of existing approaches in that it uses the full data directly, but does not utilize a Bayesian framework. It is thus computationally efficient while incorporating all sources of variability (both mutational variance and coalescent variance (cf. Huang et al. (2010))) in the estimation process. The theory underlying our method applies to unlinked Single Nucleotide Polymorphism (SNP) data, for which each site is assumed to have its own genealogy drawn from the coalescent model; however, we use simulation to show that the method also performs well for multi-locus sequence data. To describe our proposed method, we first begin with a brief overview of the coalescent model in the context of species-level phylogenetics. We use simulation to assess the performance of the method for both simulated and empirical data. We conclude with a short discussion of how the proposed method can be scaled up to larger taxon sets Downloaded from by guest on July 19, 2015 The Author (2014). Published by Oxford University Press. All rights reserved. For Permissions, please journals.permissions@oup.com 1

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3 Quartet Inference from SNPs We want to infer which of the three possible splits on quartets is the true split. One way to assess this would be to consider the Flat L1 L 2 ( ˆP ) matrix for each of the three possible splits, and measure which of the three is closest to a rank 10 matrix. To do this, we need a method to measure distances between matrices. Our choice of a distance, described below, is modeled after the approach of Eriksson (2005), who considered the problem of tree estimation from a flattening matrix obtained from the probability distribution of site patterns at the tips of a gene tree. His overall approach to estimation of the phylogeny differed from ours, however, in that he used splits of varying sizes (rather than just splits of quartets of taxa) to develop a clustering algorithm to obtain the phylogenetic estimate. We provide the details of our approach below. Let a ij be the (i, j) th entry of an m n matrix A. The Frobenius norm of a matrix A is m n A F = a 2 ij. i=1 j=1 An important property of the Frobenius norm is its characterization using the singular values of A, that is A F = p σi 2, where σ 1 σ 2 σ p 0 are the singular values of A and p = min{m, n}. The well-known low-rank approximation theorem (Eckart-Young theorem), implies that the distance from a matrix A to the nearest rank k matrix in the Frobenius norm is p A B F = σi 2. min rank(b)=k i=1 i=k+1 See Section 2.4 in Golub and Van Loan (2013) for more information about singular value decomposition. We apply this well-known result to our species tree estimation problem by defining the SVD score for a split L 1 L 2 to be SVD(L 1 L 2):= 16 ˆσ i 2, (2) i=11 where ˆσ i are the singular values of Flat L1 L 2 ( ˆP ), for i {11,...,16}. Our proposal for inferring the true species-level relationship within a sample of four taxa is thus the following. For each of the three possible splits, construct the matrix Flat L1 L 2 ( ˆP ) and compute SVD(L 1 L 2). The split with the smallest score is taken to be the true split. To quantify uncertainty in the inferred split, we implement a nonparametric bootstrap procedure as follows. For a data set consisting of M aligned sites, we re-sampled the columns of the data matrix with replacement M times to generate a new bootstrapped data matrix, and the SVD scores of the three splits are computed for this bootstrapped data matrix. This procedure is repeated B times, and the proportion of bootstrapped data sets that support each of the three possible splits provides a measure of support for that split. 2.2 Simulation Study We first use simulated data to assess the ability of SVD(L 1 L 2) to correctly identify the valid split under a variety of conditions. Before describing the simulation procedure, we first point out that while much of the currently available methodology for inferring species trees assumes that multi-locus data (e.g., aligned DNA sequences from many independent loci) are available for inference, our method is actually designed for unlinked sites, for example, for a sample of unlinked SNPs. This is because in computing the probability distribution of site patterns at the tips of the species tree, we integrate over the probability distribution of gene trees under the coalescent model, with the implicit assumption that sequence data evolve along these gene trees. Thus each site pattern is viewed as an independent draw from the distribution f(x 1 = i, X 2 = j, X 3 = k, X 4 = l S) = f(x1 = i, X2 = j, X3 = G k, X 4 = l G)f(G S)dG, where S represents the species tree (topology and speciation times) and G represents a gene tree (both topology and divergence times). True multi-locus data, however, consist of an aligned portion of the DNA that is believed to share a single underlying gene tree, and thus all sequence data within a locus are believed to have evolved from a common gene genealogy. We wish to examine the performance of our method for both unlinked SNP data and for multi-locus data, and we thus consider simulated data of two types: unlinked SNP data (e.g., each site has its own underlying gene tree) and multi-locus data (a sequence of length l is simulated from a shared underlying gene tree). Our simulation consists of the following steps: 1. Generate a sample of g gene trees from the model species tree ((1:x,2:x):x,(3:x,4:x):x), where x is the length of each branch, under the coalescent model using the program COAL (Degnan and Salter, 2005). 2. Generate sequence data of length n on each gene tree under a specified substitution model using the program Seq-Gen (Rambaut and Grassly, 1997). 3. Construct the flattening matrix for each of the three possible splits, and compute SVD(L 1 L 2) for each. 4. Repeat the above procedure (Steps 1 3) 1, 000 times and record SVD(L 1 L 2) k,k =1, 2,...,1, 000, for each split. For each of the 1, 000 data sets, generate B bootstrapped data sets and record SVD(L 1 L 2) k,b,k = 1, 2,...,1, 000; b = 1, 2,...,B for each split. Given the above simulation algorithm, there are several choices to be made at each step. In step (1), we must select the lengths of the branches, x, in the model species tree. We considered branches of length 0.5, 1.0, and 2.0 coalescent units. A branch of length 0.5 coalescent units is very short, and corresponds to a case in which there will be widespread incomplete lineage sorting, making species tree inference difficult. A branch of length 2.0 coalescent units is longer and will result in much lower rates of incomplete lineage sorting, resulting in an easier species tree inference problem. In Step (2), we need to choose the gene length, n. In simulating unlinked SNP data, we used g =5, 000 and n =1(corresponding to 5,000 unlinked SNPs) and for the multi-locus setting, we considered g = 10 and n = 500 (corresponding to 10 genes, each of length 500 sites). Further, step (2) requires choice of Downloaded from by guest on July 19,

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6 Chifman and Kubatko Table 1. Time information for the simulation study with results shown in Figure 6. All results represent the average time in seconds (over 1,000 replicates) to carry out the computation of three SVD scores for the simulated data sets, and were obtained using the UNIX time command. Branch Number of Real User System Lengths Sites Time Time Time 0.5 1, , , , , , under the GTR+I+Γ model, however, bootstrap support values for the true split are sometimes lower, with the worst results occurring when the branch lengths are short. Overall, however, the bootstrap appears to give a reliable measure of support for the true split, particularly when the model assumptions are satisfied. Figure 6 examines the performance of the method for unlinked SNP data with varying numbers of sites. In particular, unlinked SNP data sets were generated with either 1,000, 5,000, or 10,000 total sites under model species trees with branch lengths of 0.5, 1.0, or 2.0 coalescent units. These results demonstrate that the method performs well even for smaller sample sizes. However, it is clear that as the sample size becomes larger, the separation between the scores for the valid and non-valid splits increases. This is to be expected, because the matrix Flat L1 L 2 ( ˆP ) will better approximate Flat L1 L 2 (P ) for larger sample sizes. Table 1 gives timing results for the simulations carried out in Figure 6. Because the main work undertaken by the method involves counting the number of site patterns in order to build the Flat L1 L 2 ( ˆP ) matrix, the time should be approximately linear in the number of unique site patterns in the data, which is related to both the total number of sites in the data matrix and the overall scale of time represented by the phylogeny. The results in Table 1 demonstrate that the time is less than linear in the total number of site patterns, as expected, and that the computations can be carried out very rapidly (e.g., the computation of three SVD scores for data matrices of 10,000 sites takes less than 0.1 seconds). 4.2 Potential Use for Species Tree Inference These results make it clear that the SVD score is a highly accurate means of inferring the correct, unrooted phylogenetic tree among a set of four taxa. We note that the SVD score is extremely easy to compute. It requires only counting the site patterns and constructing the matrix Flat L1 L 2 ( ˆP ). Computing singular values of a matrix is a standard calculation that any mathematical or statistical software package can easily implement. Our software, SVDquartets, carries out both steps using a PHYLIP-formatted input file. Given the efficiency with which computations can be carried out in the four-taxon setting, this method is a good candidate for estimation of species trees for larger taxon sets. We propose that the method could be used in the following way. For a data set with T taxa, form all samples of 4 taxa, or sample sets of 4 taxa if T is large. For each sample of four taxa, infer the valid split using the SVD score. Using the collection of inferred valid splits, construct a species tree estimate using a quartet assembly method. Substantial previous work and software exist for the problem of quartet assembly (see, e.g., Strimmer and von Haeseler (1996); Strimmer et al. (1997); Snir and Rao (2012)). We give the results of using this method for inferring a tree consisting of several North American rattlesnake species and for inferring a tree from SNP data for several soybean species below. This method has tremendous potential to improve the set of tools available for species tree inference. Unlike summary statistics methods, which are known to be quick but fail to model variability in individual gene tree estimates, this method uses the sequence data directly, thus incorporating all sources of variability. The other existing methods based on sequence data (BEST, *BEAST, and SNAPP) all rely on Bayesian MCMC methods, and thus require long computing times and the difficult problem of assessing convergence. Our method can be carried out rapidly, and is easily parallelizable, as each quartet can be analyzed on a separate processor. Our method can handle both unlinked SNP and multilocus data, again providing an advantage over existing sequencebased methods, which can handle either SNP (SNAPP) or multilocus (BEST and *BEAST) data. Bootstrapping can be easily implemented to provide a means of quantifying support for the estimated phylogeny. However, there are several issues with this method that will need to be examined in future work. First, the number of quartets to be sampled needs to be specified in cases where the number of taxa is too large to examine all possible quartets. This number should necessarily increase with increasingly large taxon samples, but we have not yet rigorously examined how to select this. In addition, it is worth pointing out that the method estimates the topology only. In some studies, other parameters associated with the evolutionary process, such as branch lengths and effective population sizes, will also be of interest. One possibility is that the tree topology could first be estimated with this method, and then fixed in a subsequent MCMC analysis with either *BEAST or SNAPP, thus greatly reducing the complexity of that analysis. Finally, we have not yet conducted a thorough simulation study of the inferential accuracy of this method for full species tree inference, which will be the topic of future work. 4.3 Application to Rattlesnake Data The results of the analysis of the rattlesnake data set are shown in Figure 7, with bootstrap support values above 50% indicated on the appropriate nodes. In the case of the lineage tree (Figure 7(a)), the method identifies the two major species S. catenatus and S. miliarius with high bootstrap support, and additionally groups the subspecies S. c. catenatus as monophyletic. In the species tree in Figure 7(b), we again see that the method correctly identifies the two species with high bootstrap support, and is able to differentiate subspecies S. c. catenatus from a clade containing the other two subspecies within this group. Within species S. miliarius, there is not strong support for the subspecies relationships. These results are consistent with the earlier analyses of Kubatko et al. (2011), in which strong support for the delimitation of S. c. catenatus as a distinct species was found using several methods of coalescent-based species tree inference. Those analyses also found Downloaded from by guest on July 19,

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8 Chifman and Kubatko '()$ '*)$ W12 W07 W07 W14 W10 W08 W12 W15 ##$!!$ C01 C35 C12 C33 W14 W10 C35!!"!#$!!"!#$ Fig. 8. Results of the analysis of the soybean data. (a) Tree estimated by SVDquartets with bootstrap support values. (b) Maximum clade credibility tree estimated using SNAPP. and Associate Editor David Posada for helpful comments on earlier versions of this manuscript. FUNDING The authors are supported in part by National Science Foundation award DMS and in part by NIH Cancer Biology Training Grant T32-CA (J.C.). REFERENCES Allman, E. S. and Rhodes, J. A. (2008) Phylogenetic ideals and varieties for the general Markov model. Adv. in Appl. Math., 40 (2). arxiv:math.ag/ Bryant, D., Bouckaert, R., Felsenstein, J., Rosenberg, N. and RoyChoudhury, A. (2012) Inferring species trees directly from biallelic genetic markers: bypassing gene trees in a full coalescent analysis. Mol. Biol. Evol., 29 (8), Chifman, J. and Kubatko, L. S. (2014) Identifiability of the unrooted species tree topology under the coalescent model with time-reversible substitution processes, submitted; available at Degnan, J and Salter, L. (2005) Gene tree distributions under the coalescent process. Evolution 59(1), DeGeorgio, M. and Degnan, J. (2010) Fast and consistent estimation of species trees using supermatrix rooted triples. Mol. Biol. Evol. 27(3), Eriksson, N. (2005) Tree construction using singular value decompsition, in L. Pachter and B. Sturmfels, editors, Algebraic Statistics for Computational Biology, chapter 19, pgs Cambridge University Press, Cambridge, UK. Golub, G. H. and Van Loan, C. F. (2013), Matrix Computations (4th Edition), Johns Hopkins University Press. Heled, J. and Drummond, A. (2010) Bayesian inference of species trees from multilocus data. Mol. Biol. Evol., 27, Huang, H., He, Q., Kubatko, L., and Knowles, L. (2010) Sources of error for species-tree estimation: Impact of mutational and coalescent effects on accuracy and implications for choosing among different methods. Syst. Biol., 59(5), Jukes, T. and Cantor, C. (1969) Evolution of Protein Molecules. New York: Academic Press, pp Kingman,J.F.C. (1982) The coalescent. Stoch. Proc. Appl., 13, Kingman, J. F. C. (1982) Exchangeability and the evolution of large populations. Pp in G. Koch and F. Spizzichino, eds. C33 Exchangeability in probability and statistics. North-Holland, Amsterdam. Kubatko, L. S., Carstens, B. C., and Knowles, L. L. (2009) STEM: Species tree estimation using maximum likelihood for gene trees under the coalescent. Bioinformatics, 25, Kubatko, L. S., Gibbs, H. L., and Bloomquist, E. W. (2011) Inferring specieslevel phylogenies and taxonomic distinctiveness using multilocus data in Sistrurus rattlesnakes. Syst. Biol., 60, Lam, H. M., et al. (2010) Resequencing of 31 wild and cultivated soybean genomes identifies patterns of genetic diversity and selection, Nat. Genet., 42, Lee, T. H., Guo, H., Wang, X., Kim, C., and Paterson, A. H. (2014) SNPhylo: a pipeline to construct a phylogenetic tree from huge SNP data. BMC Genomics, 15(1). Liu, L. and Pearl, D. (2007). Species trees from gene trees: reconstructing Bayesian posterior distributions of a species phylogeny using estimated gene tree distributions. Syst. Biol., 56, Liu, L., Yu, L., Kubatko, L., Pearl, D., and Edwards, S. (2009) Coalescent methods for estimating phylogenetic trees. Mol. Phylogenet. Evol. 52, Liu, L., Yu L., and Pearl D. K. (2009) Maximum tree: a consistent estimator of the species tree. J. Math. Biol., 60, Liu, L., Yu, L., Pearl, D. and Edwards, S. (2009) Estimating species phylogenies using coalescence times among sequences. Syst. Biol., 58(5), Liu, L., Yu, L., and Edwards, S. (2010) A maximum pseudo-likelihood approach for estimating species trees under the coalescent model. BMC Evol. Biol., 10, 302. Maddison,W. (1997) Gene trees in species trees. Syst. Biol., 46, Rambaut, A. and Grassly, N. C. (1997) Seq-Gen: An application for the Monte Carlo simulation of DNA sequence evolution along phylogenetic trees. Comput. Appl. Biosci. 13, Rannala, B. and Yang, Z. (2003) Bayes estimation of species divergence times and ancestral population sizes using DNA sequences from multiple loci. Genetics 164, Snir, S. and S. Rao. (2012) Quartet MaxCut: A fast algorithm for amalgamating quartet trees. Mol. Phylogen. Evol. 62:,1-8. Strimmer, K., and von Haeseler, A. (1996) Quartet puzzling: A quartet maximum likelihood method for reconstructing tree topologies. Mol. Biol. Evol. 13, Strimmer, K., Goldman, N., and von Haeseler, A. (1997) Bayesian probabilities and quartet puzzling. Mol. Biol. Evol. 14, Tavare, S. (1986) Some probabilistic and statistical problems in the analysis of DNA sequences, Lectures on Mathematics in the Life Sciences (American Mathematical Society) 17, Than, C. and Nakhleh, L. (2009) Species tree inference by minimizing deep coalescences. PLoS Computational Biology, 5(9), e W08 C12 W15 C01 Downloaded from by guest on July 19,